Street Stall Economy in China in the COVID-19 Era: Dilemmas and the International Experience of Promoting the Normalization of Street Stall Economy

Compared with those major policies that need to be practiced over the years, the street stall economy is more like a special means after the epidemic, resulting in a “short and brilliant” heat. Nevertheless, the street stall economy revives is facing several dilemmas. This paper reveals the dilemma of the prosperity and development of the stall economy before and after the epidemic, followed by the international experience and enlightenment of promoting the normalization of street stall economy, ranging from street vendor’s legal status and road administrative promotion to street food safety and environmental protection. To sum up, employment is the foundation of people’s livelihood and the source of wealth, hence, stall economy plays an indispensable role to create a win-win working world and promote the formation of a sustainable economic

Influence of Al and Al_2O_3 Nanoparticles on the Thermal Decay of 1,3,5-Trinitro-1,3,5-triazinane (RDX): Reactive Molecular Dynamics Simulations

Metallic additives, Al nanoparticles in particular, have extensively been used in energetic materials (EMs), of which thermal decomposition is one of the most basic properties. Nevertheless, the underlying mechanism for the highly active Al nanoparticles and their oxidized counterparts, the Al_2O_3 nanoparticles, influencing the thermal decay of aluminized EMs has not fully been understood. Herein, we explore the influence of Al and Al2O3 nanoparticles on the thermal decomposition of 1,3,5-trinitro-1,3,5-triazinane (RDX), one of the most common EMs, based on large-scale reactive force field molecular dynamics simulations within three heating schemes (constant-temperature, programmed, and adiabatic heating). The presence of Al nanoparticles significantly reduces the induction time and energy required to activate the RDX decay and greatly increases energy release. The fundamental reason for these results is that Al changes the primary decay pathway from the unimolecular N–NO_2 scission of RDX to bimolecular barrier-free or low-barrier Al-involved reactions and possesses a strong O-extraction capability and a moderate one to react with C/H/N. It is also responsible for the growth of the Al-containing clusters. In addition, Al_2O_3 nanoparticles can also demonstrate such catalysis capability but contribute less to the enhancement of energy release. Moreover, the detailed evolutions of key thermodynamic properties, intermediate and final gaseous products, and Al-containing products are also presented. Besides, under the programmed heating and adiabatic heating conditions, the catalysis of the Al and Al_2O_3 nanoparticles becomes more distinct. Therefore, many properties of aluminized EMs are expected to well be understood by our simulation results

GuardNN: Secure DNN Accelerator for Privacy-Preserving Deep Learning

This paper proposes GuardNN, a secure deep neural network (DNN) accelerator, which provides strong hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows that the architecture and protection can be customized for a specific application to provide strong confidentiality and integrity protection with negligible overhead. The design of the GuardNN instruction set reduces the TCB to just the accelerator and enables confidentiality protection without the overhead of integrity protection. GuardNN also introduces a new application-specific memory protection scheme to minimize the overhead of memory encryption and integrity verification. The scheme shows that most of the off-chip meta-data in today's state-of-the-art memory protection can be removed by exploiting the known memory access patterns of a DNN accelerator. GuardNN is implemented as an FPGA prototype, which demonstrates effective protection with less than 2% performance overhead for inference over a variety of modern DNN models

MgX: Near-Zero Overhead Memory Protection with an Application to Secure DNN Acceleration

In this paper, we propose MgX, a near-zero overhead memory protection scheme for hardware accelerators. MgX minimizes the performance overhead of off-chip memory encryption and integrity verification by exploiting the application-specific aspect of accelerators. Accelerators tend to explicitly manage data movement between on-chip and off-chip memory, typically at an object granularity that is much larger than cache lines. Exploiting these accelerator-specific characteristics, MgX generates version numbers used in memory encryption and integrity verification only using on-chip state without storing them in memory, and also customizes the granularity of the memory protection to match the granularity used by the accelerator. To demonstrate the applicability of MgX, we present an in-depth study of MgX for deep neural network (DNN) and also describe implementations for H.264 video decoding and genome alignment. Experimental results show that applying MgX has less than 1% performance overhead for both DNN inference and training on state-of-the-art DNN architectures

Bi-level Actor-Critic for Multi-agent Coordination

Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforcement learning (MARL) methods treat agents equally and the goal is to solve the Markov game to an arbitrary Nash equilibrium (NE) when multiple equilibra exist, thus lacking a solution for NE selection. In this paper, we treat agents \emph{unequally} and consider Stackelberg equilibrium as a potentially better convergence point than Nash equilibrium in terms of Pareto superiority, especially in cooperative environments. Under Markov games, we formally define the bi-level reinforcement learning problem in finding Stackelberg equilibrium. We propose a novel bi-level actor-critic learning method that allows agents to have different knowledge base (thus intelligent), while their actions still can be executed simultaneously and distributedly. The convergence proof is given, while the resulting learning algorithm is tested against the state of the arts. We found that the proposed bi-level actor-critic algorithm successfully converged to the Stackelberg equilibria in matrix games and find an asymmetric solution in a highway merge environment

A Trusted Real-Time Scheduling Model for Wireless Sensor Networks

Heterogeneous multicore and multiprocessor systems have been widely used for wireless sensor information processing, but system energy consumption has become an increasingly important issue. To ensure the reliable and safe operation of sensor systems, the task scheduling success rate of heterogeneous platforms should be improved, and energy consumption should be reduced. This work establishes a trusted task scheduling model for wireless sensor networks, proposes an energy consumption model, and adopts the ant colony algorithm and bee colony algorithm for the task scheduling of a real-time sensor node. Experimental result shows that the genetic algorithm and ant colony algorithm can efficiently solve the energy consumption problem in the trusted task scheduling of a wireless sensor and that the performance of the bee colony algorithm is slightly inferior to that of the first two methods

A two-level cache for distributed information retrieval in search engines,”

To improve the performance of distributed information retrieval in search engines, we propose a two-level cache structure based on the queries of the users' logs. We extract the highest rank queries of users from the static cache, in which the queries are the most popular. We adopt the dynamic cache as an auxiliary to optimize the distribution of the cache data. We propose a distribution strategy of the cache data. The experiments prove that the hit rate, the efficiency, and the time consumption of the two-level cache have advantages compared with other structures of cache

A Two-Level Cache for Distributed Information Retrieval in Search Engines

To improve the performance of distributed information retrieval in search engines, we propose a two-level cache structure based on the queries of the users’ logs. We extract the highest rank queries of users from the static cache, in which the queries are the most popular. We adopt the dynamic cache as an auxiliary to optimize the distribution of the cache data. We propose a distribution strategy of the cache data. The experiments prove that the hit rate, the efficiency, and the time consumption of the two-level cache have advantages compared with other structures of cache